In this decade, with the rise of data science accompanying the growth of e-commerce, many technologies have been developed. An example of these technologies is Blockchain, which has appeared to overcome security problems potentially. This research assesses Blockchain's implementation in supply chains through a methodology that uses deep learning and agent-based simulation. A case study was utilized to observe and validate research developments. The unique method predicts intrusions by using deep learning, and agent-based modeling reproduces artificial but convincing agents (e.g., customers, companies, hackers, and cyber pirates) in a computer-generated market. Trust and other relationships are systematically captured to represent Blockchain additions. Once again, the agent-based simulation model's environment permits hypothetical interactions and emergent features by coordinating supply and demand for business-to-consumer e-commerce events. The case study based on a real environment shows that the proposed method can determine the feasibility of the business model and Blockchain implementation's potential contributions.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Obeidat, Mohammad, "Assessing the Potential of Implementing Blockchain in Supply Chains using Agent-Based Simulation and Deep Learning" (2020). Electronic Theses and Dissertations, 2020-. 390.
Restricted to the UCF community until December 2021; it will then be open access.